BibSonomy publications for /user/goalscoringsuperstarhero/extractionFri Apr 18 12:04:00 CEST 2008Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and TreatmentAn Empirical Model of Multiword Expression Decomposability2003extraction decomposition multiword This paper presents a construction-inspecific model of multiword expression decomposability based on latent semantic analysis. We use latent semantic analysis to determine the similarity between a multiword expression and its constituent words, and claim that higher similarities indicate greater decomposability. We test the model over English noun-noun compounds and verb-particles, and evaluate its correlation with similarities and hyponymy values in WordNet. Based on mean hyponymy over partitions of data ranked on similarity, we furnish evidence for the calculated similarities being correlated with the semantic relational content of WordNet.Tue Mar 11 18:46:07 CET 2008K-CAPconf/kcap/2005137-144Finding new terminology in very large corpora.2005extraction lpm collocation term lsm corpus Tue Mar 11 18:45:30 CET 2008HLT/EMNLPconf/naacl/2005Paradigmatic Modifiability Statistics for the Extraction of Complex Multi-Word Terms.2005lpm linguistic extraction corpus ngram term Tue Mar 11 18:22:21 CET 2008Sydney, Australia44th Annual Meeting of the Association for Computational LinguisticsJuly785--792You Can't Beat Frequency (Unless You Use Linguistic Knowledge) -- A Qualitative Evaluation of Association Measures for Collocation and Term Extraction2006term lsm lpm corpus extraction ngram frequency collocation In the past years, a number of lexical association measures have been studied to help extract new scientific terminology or general-language collocations. The implicit assumption of this research was that newly designed term measures involving more sophisticated statistical criteria would outperform simple counts of cooccurrence frequencies. We here explicitly test this assumption. By way of four qualitative criteria, we show that purely statistics-based measures reveal virtually no difference compared with frequency of occurrence counts, while linguistically more informed metrics do reveal such a marked difference.